energies

Article
Impact of the COVID-19 Lockdown on the Electricity System of
Great Britain: A Study on Energy Demand, Generation, Pricing
and Grid Stability
Desen Kirli * , Maximilian Parzen and Aristides Kiprakis

Institute for Energy Systems, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, UK;
m.parzen@sms.ed.ac.uk (M.P.); kiprakis@ed.ac.uk (A.K.)
* Correspondence: desen.kirli@ed.ac.uk

Abstract: The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns
in electricity consumption, challenging the system operations of forecasting and balancing supply
and demand. This is mainly due to the mitigation measures that include lockdown and work
from home (WFH), which decreased the aggregated demand and remarkably altered its profile.
Here, we characterise these changes with various quantitative markers and compare it with pre-
lockdown business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the
generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed.
An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies
is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that
a shift to WFH will result in a net benefit for flexible stakeholders, such as consumers on variable
tariffs. Furthermore, the analysis illustrates a need for faster and more frequent balancing actions, as
a result of the increased share of renewable energy in the generation mix. This new equilibrium of
energy demand and supply will require a redesign of the existing balancing mechanisms as well as



the longer-term power system planning strategies.



Citation: Kirli, D.; Parzen, M.; Keywords: electricity system; COVID-19; electricity demand; energy; demand; behaviour; lockdown;
Kiprakis, A. Impact of the COVID-19 electricity pricing
Lockdown on the Electricity System
of Great Britain: A Study on Energy
Demand, Generation, Pricing and
Grid Stability. Energies 2021, 14, 635.
1. Introduction
https://doi.org/10.3390/en14030635
The outbreak of coronavirus disease 2019 (COVID-19) led to a lockdown on Wednes-
Received: 11 November 2020 day, the 23rd of March 2020 in the United Kingdom (UK). The government instructed that
Accepted: 18 January 2021 people should leave their homes only for purchasing necessities and exercising. People
Published: 27 January 2021 were only allowed to go to work if working from home (WFH) was not possible. Failing to
follow the new lockdown measures would lead to fines [1]. The strict lockdown ended on
Publisher’s Note: MDPI stays neu- Sunday, 10th of May and was replaced with a range of looser measures for the containment
tral with regard to jurisdictional clai- of the disease. However, WFH has become the new norm. These measures lead to a
ms in published maps and institutio- disruptive change in the electricity demand and influenced the wider energy sector. Energy
nal affiliations. companies in the UK warned about potential blackouts [2]. The analysis of this high impact
and low probability event is significant as any adverse effects on the electricity sector due
to future pandemics or lockdowns could be forecast using the insights of this analysis.
Studies such as [3] investigate ways to improve the power system resilience for high
Copyright: © 2021 by the authors. Li-
censee MDPI, Basel, Switzerland.
impact and low probability events under future climate and extreme weather conditions.
This article is an open access article
However, the impact of a pandemic such as the one experienced with COVID-19 is still
distributed under the terms and con-
unclear. The changes and trends in energy due to the pandemic are identified for de-
ditions of the Creative Commons At- mand [4,5], generation, grid stability [5] and various power markets [6,7]. Most of the
tribution (CC BY) license (https:// aforementioned analyses quantify changes by determining absolute or percentage change
creativecommons.org/licenses/by/ between pre- and post-lockdown periods.
4.0/).

Energies 2021, 14, 635. https://doi.org/10.3390/en14030635 https://www.mdpi.com/journal/energies

Energies 2021, 14, 635 2 of 25

As noted in [4], all analyses should be addressed with caution, since comparing
different timeframes in power systems is a challenge due to various distorting factors that
play a role such as weather, human behaviour and economic climate.
As a result, this study seeks to analyse the changes in demand, generation, grid stability
and market prices in a quantitative manner where changes are striking and choosing a
qualitative approach where the difference is ambiguous.
The main contribution of this paper is the systematic observation and analysis of
the effects of the COVID-19 lockdown in Great Britain on demand and operation of the
electrical power network, during its early weeks. Secondarily, we present the electricity
data pipeline employed for our analysis, which is also made available as open source.
The main highlights of this work are the following:
• This analysis characterises the changes in aggregate demand magnitude and profile
due to the lockdown with various quantitative markers such as load duration curve,
statistical distribution analysis and others.
• The ripple effects on the generation portfolio, system frequency, forecasting accuracy
and imbalance pricing are also evaluated.
• The effect of the lockdown on domestic consumers on a variable energy tariff is
identified and over 70 occurrences of negative pricing were detected.
• The implications of the lockdown are discussed for different stakeholders including
generators, industrial and commercial consumers, domestic consumers on both fixed
and variable tariffs, aggregators and demand-side response providers.
• The possibility of the lockdown data being an outlook for the future electricity sys-
tem in terms of flatter demand profile and increased contribution from the variable
renewable generators is discussed.
• The electricity data extraction and pre-processing pipeline that can be used in a variety
of similar studies is presented.

2. Methodology
In order to analyse the impact of the COVID-19 lockdown on the electricity market,
a systematic approach is used which involved creating an efficient pipeline to extract the
target data, pre-process, analyse and visualise. In this section, the methodology used in the
pipeline is explained. The instructions for future use are detailed using a flow diagram.
The Python code employed can be accessed at the GitHub repository [8]. Both pure Python
(i.e., py) and interactive Python notebook (i.e., ipynb) formats are made available. This
pipeline was used to create a clean and filtered dataset that consists of all of the data used
in the plots and analyses in this paper. This dataset is deposited in a public DataShare
repository—see [9].

2.1. Function of the Data Pipeline Code

As this study involves a comparative analysis, various actions, such as cleaning the
data and calculating the percentage differences, had to be repeated for different data sets
such as frequency and demand. In order to minimise the time spent from the import of the
data to the analysis and visualisation stages, a data pipeline on Python was programmed to
fetch the data directly using the application programming interface (API) of the provider—
see [8].
This process is also commonly referred to as scripting, extracting and scraping the
data. The pipeline is used for analysing the system frequency, demand, generation and
other types of data and all results are presented in Section 3. It performs the following
steps listed below.
1. Import the data from the source webpage using the API user key.
2. Identify the keywords and group the data.
3. Create weekly data frames (according to the Monday-to-Sunday convention (i.e., ISO 8601)).
4. Check for zeros, invalid or duplicate data.
5. Label and discard the columns that are not of interest.
Energies 2021, 14, 635 3 of 25

6. Adjust the date and time format (e.g., change from half-hourly settlement period
convention (where 01:00 is denoted by 2) to time).
7. Save the adjusted data in CSV format with an automated title
(DataLabel_Week_starting_StartDate.csv).
8. Calculate statistical and other quantitative descriptors such as mean, peak-to-mean
ratio, etc.
9. Produce comparative visualisations of the data.

2.2. Other Uses of the Data Pipeline

In most of our analysis cases, the data source is the Balancing Mechanism Reporting
Service (BMRS) [10]. The website [11] provides open-source data that are used for balancing
and settling the GB electricity system.
The data provided (e.g., system demand, frequency, generation by fuel type, etc.) are
used for reaching trading decisions and analysing the dynamics of market volumes and pric-
ing. Since we believe that this pipeline which enables easy data extraction, pre-processing
and visualisation may be useful for both academic and industrial researchers who are
interested in electricity market dynamics, regulation, trading and forecasting. A flowchart
is presented in Figure 1 which displays the steps required to replicate the results.

(1) Register (2) Access & Input (3) Results

• Register on the ELEXON • Access the pipeline on • Comparison of the selected
website GitHub time frames
• Obtain API key • Input API key into the code • Calculation of statistic
• Select the desired time descriptors
frame for analysis • Looped data visualisation
with automated legends
• Auto-save in CSV format
with automated names with
DataLabel and StartDate.

Figure 1. The flowchart displaying the steps for employing the data pipeline which are registering, accessing the code
repository and producing the results in order.

As shown in Figure 1, there are two prior steps to reaching the results stage where the
quantitative data descriptors are calculated and the results are visualised in a comparative
manner. The first step is to obtain an API key by registering on the Elexon website [11]—a
more detailed guidance on this is provided by Elexon [10]. Then, the pipeline can be
accessed from the GitHub website [8] and the API key obtained by the user should be input.
The default timeframes are set to the pre- and post-lockdown weeks used for this analysis
which commence on the 2nd and 23rd of March 2020. However, these can be adjusted
to the timeframe of interest. In addition to the system demand, this pipeline can be used
for all other data types provided by Elexon which are listed in [10]. The code can also be
modified to refer to any other website to execute a direct data extraction using API.

3. Results
In order to effectively present the impact of the COVID-19 lockdown on the GB elec-
tricity system, four main categories are identified and analysed: (1) the changes in demand
profile and volume, (2) generation portfolio; renewable and conventional generation shares,
Energies 2021, 14, 635 4 of 25

(3) forecasting and grid stability indicators and lastly (4) market prices, including day-
ahead wholesale market, system imbalance and variable prices for the domestic consumers.
Furthermore, the grid stability subsection inspects imbalance volume, system frequency
and the loss of load probability.

3.1. Demand Profile

This subsection analyses and quantifies the changes in the electricity consumption
caused by the COVID-19 mitigation actions such as the lockdown. On the 23rd of March
2020, the UK Government recommended WFH and closed public spaces such as pubs,
restaurants and sport facilities [12]. The impact on the electricity demand is shown by the
purple profile and compared to a pre-lockdown week in Figure 2. The figure illustrates
that the overall demand decreased as majority of the commercial users (e.g., factories,
businesses, etc.) shut down. In addition to the demand reduction, the lockdown also
Aggregated System
influenced theDemand pre- and
consumption post-COVID-19
pattern Mitigation
which results Actions
in a changed load profile shape.
Date
2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30

45000
,

40000
,
Demand (MW)

35000
,

30000
,

25000
,

20000
,
2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09
Date

Figure 2. Aggregated system demand before (w/c 02/03/20) and after (w/c 23/03/20) the coronavirus 2019 (COVID-19)
actions. Changes are observed in demand magnitude and profile with decreased demand after the lockdown.

Before assessing the impact of the lockdown on the power demand profile, it is im-
portant to quantify any change that was caused by any other external variables. The most
important factor affecting the power demand in GB is the weather, as it is highly corre-
lated with the energy demand for heating. In order to assess the weather-related impact,
the average temperature in Britain is compared for pre- and post-lockdown days. The daily
GB average temperature is provided by National Grid which uses data from six weather
stations around Britain [13]. Figure 3 shows the temperature difference with an average
value of 2.2 ◦ C. According to Thornton et al. [14], 1 ◦ C difference in temperature results in
approximately 1% change in the electricity consumption. Thus, the weather-related impact
is expected to reduce the consumption in the post-lockdown week by 2.2% when com-
pared with the pre-lockdown week. The maximum temperature difference of 4.2 ◦ C occurs
between the 5th and 26th of March which would decrease the post-lockdown demand
by 4%.
Energies 2021, 14, 635 5 of 25

Date
2020-03-22 2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28 2020-03-29 2020-03-30
12

10
Avergae Daily Temperature (⁰C)

0
2020-03-01 2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09
Date

Figure 3. Average temperature data in Britain for before (w/c 02/03/20) and after (w/c 23/03/20) the lockdown. The aver-
age difference between the pre- and post-lockdown weeks is 2.2 ◦ C.

As displayed in Figure 4, load duration curves show the base and peak demand
by visualising the relationship between sorted demand (i.e., ranked descending) and
exceedence. Whilst the base demand decreases by 10%, the peak and mean demand
drastically drop by 20% and 24%, respectively, following the start of the lockdown. As the
experienced decrease of power demand is an order of magnitude higher than what would
be expected to be due to temperature alone, it can be concluded that the change in demand
is predominantly driven by the lockdown rather than the change in the weather conditions.

Load Duration Curve - Pre and Post

Pre
Post

40000
,
Demand (MW)

30000
,

20000
,

10000
,

0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95
Exceedence [%]

Figure 4. Load duration curve for pre- and post-lockdown actions (w/c 02/03/20 and 23/03/20)
showing the decrease in the post-action scenario with the highest decrease in peak and lowest in the
base load.
Energies 2021, 14, 635 6 of 25

The changes in the demand profile for peak, mean and base load are shown in Table 1.
The decrease in the energy demand is also observed as the area of the red plot (i.e., post-
lockdown) is smaller than the blue plot (i.e., post-lockdown) in Figure 4. A constant
demand would result in a flat load duration curve. This is evident in Figure 4, where the
post-lockdown plot (in red) is flatter than the one before the lockdown. This is a result
of the greater decrease in peak values in comparison to the base. Flattening the demand
curve means the prime time peaks such as the morning pick-up and the evening demand
surge are now less pronounced. Such peaks increase the difficulty of matching demand
and supply, puts the grid under stress and also increases the stress on thermal generation
and storage to meet the demand. The ripple effects include congestion and high imbalance
and transmission charges.

Table 1. Changes in demand profile using the data from the load duration curves.

Peak Load Mean Load Base Load

Profile % % %
(MW) (MW) (MW)
Pre 46,425 33,868 22,982
Post 38,585 −20.31% 27,294 −24.08% 20,795 −9.5%

Figure 5 displays the pre- and post-demand histograms where the post-action demand
is shifted to the left, indicating lower loads. The peak for the post-action demand shows
that the range of the most frequently occurring demand values is now narrower, meaning
there is less variation. Otherwise, the pre-action demand shows a more dispersed profile
with a bi-modal distribution. The concentration and higher rate of occurrence around
26,000 MW also reflect that the time series of demand is flatter.

1e-4 1e-4
0.0002.00 0.0002.00

0.0001.75 0.0001.75
Normalised Rate of Occurrence

0.0001.50 0.0001.50

0.0001.25 0.0001.25

0.0001.00 0.0001.00

0.0000.75 0.0000.75

0.0000.50 0.0000.50

0.0000.25 0.0000.25

0.000000
20,000 25,000 30,000 35,000 40,000 45,000 20,000 25,000 30,000 35,000 40,000 45,000

Pre Demand (MW) Post Demand (MW)

Figure 5. Comparison of pre and post-lockdown demand histograms with normalised occurrences. The range of colours
from yellow to dark blue correspond to the highest and lowest values. The post-lockdown shape is smoother and occurrence
concentrates only once around 26 MW. Whereas, the pre-lockdown distribution has multiple modes as shown by the
running average curve in orange. The plot on the left represents w/c 02/03/20 and the one on the right represents w/c
23/03/20.

Figure 6 uses a ratio of the standard deviation over the mean in order to quantify
the variation in the consumption profile. It suggests an overall lower variation in the
post-COVID-19 profile with a largest variation in the morning with respect to the mean.
The evening variation coefficient is remarkably lower. The overall standard deviation
of the post-lockdown week is a third of the pre-lockdown week. Hence, it supports the
observation that the post-lockdown demand profile is flatter. Figure 6 reflects an hour
Energies 2021, 14, 635 7 of 25

delay in the morning peak (i.e., 8 to 9 a.m.) and a changed evening profile. Regarding
the evening demand surge, it should also be noted that Figure 2 shows steeper evening
peaks as the morning peaks become less pronounced for the lockdown week. For instance,
on average the pre demand used to have a 7500 MW increase over 4 h to the evening peak
whereas the post-action demand escalates by 9500 MW in 5 h. Despite the longer increase
time, the relative increase is higher.

Comparison of Variation Coefficient in Pre and Post Demand

Pre Post

0.16 0.16

0.14 0.14
Variation Coefficient

Variation Coefficient
0.12 0.12

0.10 0.10

0.08 0.08

0.06 0.06

0.04 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0.04 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
time time
Figure 6. Comparison of variation coefficient to visualise the changes in system demand. The magnitude of the variation
coefficient decreases in the post-case, the morning peak is delayed by an hour and the variation in the evening peak is less
pronounced. The plot on the left represents w/c 02/03/20 and the one on the right represents w/c 23/03/20.

It could be speculated that this is due to the human behaviour change as the common
9 a.m. to 5 p.m. working routine may not apply to all WFH. Hence, the delay in the
morning peak may suggest a later wake-up time and earlier pick-up in the evening may be
associated with the increased demand for heating, cooking and similar.

3.2. Generation Portfolio

This subsection focuses on how the changes in demand pattern and magnitude affected
the generation portfolio. In particular, the lower demand led to a higher share of renewable
energy sources (RES), namely wind and solar and consequently impacted the conventional
generation portfolio in the power system.

3.2.1. Renewable Energy Contribution

The share of generation from RES increased following the COVID-19 mitigation actions
and consecutive changes in the electricity demand: generators in European electricity mar-
kets are scheduled in merit order, which means that the generators with the lowest marginal
costs are supplying the power demand. RES generators in particular have marginal costs
close to zero. Hence, they are usually scheduled before other generation technologies [10].
Therefore, their output is not restricted by the pandemic circumstances and the changes in
the demand profile and magnitude result in the increase of the RES contribution.
On the other hand, these increasing RES shares cannot be easily detected by observed
data as they additionally depend on weather conditions. Changing weather conditions
make it challenging to compare pre- and post-lockdown data sets as it is not possible to de-
couple the effect of weather conditions and RES generation. This is because the aggregated
RES output from various locations is used for this study. Therefore, Figure 7 illustrates
a qualitative example which keeps the RES generated output stable (i.e., unaffected by
varying weather conditions), representing constant solar and wind conditions for a high
Energies 2021, 14, 635 8 of 25

and low demand case, respectively. The scale for demand and generation in Figure 7, rep-
resents approximately real observed data from GB. In the example, the demand reduction
of 25%, which was recognised in the first week after the lockdown, led to an absolute RES
share growth of 8%.
As a result of Figure 7 and the requirement of scheduling RES before conventional
generators, the average demand reduction leads to higher RES shares in the long term.
If the data from Figure 7 are representative for longer periods, they further indicate that
the RES share could increase in the range of 5–10% in the GB system due to the lower
demand profile.

Impact of demand changes on the RES share

High demand case Low demand case

40000
34 GWGW 25 % 40000
34 GWGW 25 %

30000 GW
27 GW 30000 GW
27 GW

Δ 8% 33 %

25 %
8.5 GW 8.5 GW

RES RES
25.0%

Demand Generation RES share

Figure 7. An illustrative example of the impact of demand changes on the renewable energy sources (RES) share for typical
UK RES conditions. The RES share grows under the assumption of constant RES output before and after the demand
changes. The RES shares are kept constant in order to point out the impact of a lockdown.

3.2.2. Impact on the Conventional Generation Portfolio

The conventional generation portfolio, including all generation assets with marginal
cost above solar and wind technologies, are expected to decrease in operation time com-
pared to the business-as-usual case. The reason is beside the lower total demand, the higher
share of RES assets on the market which are dispatched first because of the merit order.
The decrease in operation time particularly affects the higher marginal cost generators such
that nuclear and hydro plants are less affected. However, the higher share of RES assets
causes an additional need for more flexibility in the power system—similar to the one
provided by the natural gas plants [15]. If the inflexible generators sustain their behaviour
of not turning down their generation at high RES times, there will be more events when
the price is negative [16,17]. Therefore, a flexible gas or biomass plant could be preferred
over a less flexible coal plant.
As result, the total generation portfolio is likely to reduce carbon emissions by the
increase of the RES share and the push out of inflexible coal plants.
Energies 2021, 14, 635 9 of 25

3.3. Forecasting and Grid Stability

The aspects related to forecasting and grid stability are discussed in this section.
These include factors such as the deviations in system frequency, imbalance volume and
load forecast error of the system operator. Additionally, it investigates the loss of load
probability as indicators of the reserve scarcity and increased stress in the grid.

3.3.1. Deviations in System Frequency

The system frequency varies continuously and reflects the real-time discrepancies
between system demand and total generation. Frequency increases when there is too much
generation or too little demand on the system and vice versa. Similar to other system
operators, National Grid has a legal obligation to maintain system frequency within the
range of 49.5 and 50.5 Hz [11]. This requires the system operators to accurately forecast
demand and schedule generation accordingly whilst keeping a fast-response reserve or
a demand-side action available for any unforeseen changes. Nonetheless, the changes in
frequency are also related to the system inertia. Introduction of most RES generators such
as solar panels and some types of storage such as batteries resulted in a decreased system
inertia and consequently a less stable system frequency [18]. Demand-side response (DSR)
and other balancing services can be activated to ensure operation within the permitted
ranges of frequency [19]. Currently, there is research assessing whether the wind and
solar power plants could deploy synthetic inertia in order to compensate for this problem
associated with RES generation [20–22].
An abrupt change in the demand, like the one due to the lockdown, is expected to
negatively affect the frequency. In this case, the frequency is expected to rise as the decrease
in demand would result in a surplus of generation. When analysed in its barest form,
no significant discrepancies are observed for a pre- and post-lockdown week. The mean,
minimum and maximum frequency values also conclude an insignificant difference below
0.2% difference—see Table 2. Despite the fact that both the minimum and maximum
frequency values for the post-action week are higher than the pre-action week, the analysis
is not sufficient to state a remarkable frequency variation due to the lockdown or perhaps
more likely that it shows that the frequency was maintained well by the National Grid.

Table 2. Comparison of descriptors to quantify the changes in the system frequency pre- and post-
lockdown mitigation actions. The increase in the post-action minimum and maximum frequency is
highlighted by the red text colour.

Data Mean Min Max

Pre (Hz) 49.998804 49.736000 50.207000
Post (Hz) 49.998657 49.775000 50.267000

Hence, a normalised occurrence study is carried out to assess the distribution of system
frequency in Figure 8. The distribution for the post-action data has more defined peak
around 50 Hz and its distribution width from 49.9 to 50.1 Hz is narrower. This suggests
that the frequency was maintained within a stricter window than the pre-action week.
The concentration of occurrence is below 50 Hz pre-lockdown whilst values above the
nominal values are recorded more frequently after the lockdown (where the range of
colours from yellow to dark blue in Figure 8 represents the highest to lowest occurrence,
respectively). Hence, this displays a shift in the system frequency distribution, pointing
out the increase in high frequency records.
Energies 2021, 14, 635 10 of 25

Frequency Histograms for Pre- and Post-COVID-19 Actions

6 6

5 5
Normalised Occurance

4 4

3 3

2 2

1 1

0 49.8 49.9 50.0 50.1 50.2 0 49.8 49.9 50.0 50.1 50.2
Pre Frequency (Hz) Post Frequency (Hz)
Figure 8. Comparison of pre- and post-action system frequency histograms. The plot on the left represents w/c 02/03/20
and the one on the right represents w/c 23/03/20. The post-action frequency distribution is concentrated more in the range
of 49.9 to 50.1 Hz.

One reason for this may be the decreased load profile, resulting a generation surplus,
thus increasing the frequency—as discussed in Section 3.1. A peak-to-mean analysis is
performed on both pre- and post-action data to compare the degree of variation—as shown
in Figure 9. The frequency data is indexed by the time of the day. The most significant
observation is regarding the high peak-to-mean ratio calculated for 8 p.m. for the post-
lockdown week. This may be because of the unforeseen changes in the shape of the
consumption profile—this is discussed in Section 3.1.

Time-indexed comparison of peak-to-mean ratio in pre and post-action system frequency

0.0055
Pre 0.0055
Post

0.0050 0.0050

0.0045 0.0045
Peak-to-Mean Ratio

Peak-to-Mean Ratio

0.0040 0.0040

0.0035 0.0035

0.0030 0.0030

0.0025 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0.0025 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time Time
Figure 9. Comparison of hour-indexed peak-to-mean ratios for system frequency, showing an increase in 8 p.m.
high frequency occurrence post-lockdown. The plot on the left represents w/c 02/03/20 and the one on the right represents
23/03/20.
Energies 2021, 14, 635 11 of 25

3.3.2. Load Forecast Error

The effect of the COVID-19 lockdown on the short-term load forecasts are analysed in
the GB power system. In contrast to mid and long-term forecast, which make predictions
months and years prior to the event, the short-term load forecasts have a shorter outlook
which range from one hour to weeks before the settlement period [23,24]. Short-term
load forecasting plays an important role in scheduling the power plants efficiently in
the electricity market, as it is essential for economic dispatch and unit commitment [25].
As a result, an improved forecast accuracy leads to a more reliable and affordable power
system [23].
Two different short-term load forecasts are analysed in this study, the day-ahead total
load forecast (DAF) and transmission system final load forecast (TSF). The DAF and TSF
differ in methodology and forecast length. With regards to the methodology, the DAF
represents a forecast for the total load in the power system, which equals the sum of
generated power on both transmission and distribution networks, whereas TSF is a load
forecast which is equal to the sum of generation present on the transmission network.
However, this includes generation from pumped hydro storage and embedded large power
plants at the distribution network level. Therefore, TSF is interpreted as the net demand,
usually published by the TSO for market clearing, while the DAF represents the estimated
actual total load in the power system.
Regarding the forecast length, which is the duration from the forecast publication
until the operation time, it varies for DAF and TSF, as seen in Figure 10. DAF is published
only once a day and predicts the next-day average demand in each settlement period.
Hence, the forecast length varies from 12–36 h. Similarly, TSF outputs a day-ahead forecast.
However, forecasts for each settlement period are updated until the final TSF which predicts
the next average demand in the next settlement period, 1 h and 15 min ahead.

Figure 10. Illustration of the forecast lengths for the total day-ahead load forecast (DAF) and the final transmission system
load forecast (TSF).

DAF and TSF forecast errors reveal different characteristics due to the lockdown.
The DAF forecast is improved while the TSF forecast does not reflect clear changes. This is
shown in Figure 11. The forecast error is evaluated by one of the most common performance
indicators, namely the mean absolute percentage error (MAPE) [23,25]. MAPE functions
well as a forecast performance indicator when employing historical data. Nevertheless,
for prediction model selection and estimation it is biased [26]. As only historical data are
analysed in this study, this makes MAPE a suitable indicator.
Energies 2021, 14, 635 12 of 25

MAPE is defined as a summation of forecast errors, where each error is weighted to

the actual load. This is shown in Equation (1) where y represents the actual load, ŷ is the
load forecast and N is the number of forecasts.
N yi,actual − − − ŷi, f orecast
1
MAPE =
N ∑ yi,actual
(1)
i =1

In general, the longer the forecast length is for the same point in time, the higher the
forecast error becomes [27].

Day-Ahead forecast error

10.0%

9.0%

8.0%
MAPE

7.0%

6.0%

5.0%
Lock-down
Wednesday, CW 13
4.0%

3.0%
2 3 4 5 6 7 8 9 10 11 12 13
Calendar week

(a) DAF error.

Transmission System forecast error

2.50%

2.00%
Mon - Sun 2020
Wed - Fri 2020
MAPE

1.50%
Mon - Sun 2019
Wed - Fri 2019
1.00% Lock-down
Wednesday, CW 13

0.50%
2 3 4 5 6 7 8 9 10 11 12 13
Calendar week

(b) TSF error.

Figure 11. Weekly aggregated total DAF and final TSF error, for the timeframe from (i) Monday to
Sunday and (ii) Wednesday to Friday for 2019 and 2020. Note: The Wednesday to Friday frame
was used to show the impact of the lockdown from a workday perspective. The lockdown led to a
significant improvement of the DAF, while the TSF experience minor changes.

Therefore, the DAF constitutes a higher forecast error than the TSF. With regards to
the lockdown effects, the improved forecast accuracy for the DAF is especially remarkable
when distilling the timeframe on the weekdays from Wednesday to Friday, which starts on
the day the lockdown was initiated. The typical working week differs from the lockdown
working week and the weekend is more similar to the lockdown weekend. Hence, the effect
of the lockdown would be overlooked if the analysed timeframe was for the whole week.
On the contrary, no such effects are observed for TSF, which implies that the shorter-term
forecasts are less subject to the lockdown effect.
The change in the forecast error cannot be solely traced back to the lockdown, since
the forecast error is affected by many factors. In [27], a list of components affecting the
Energies 2021, 14, 635 13 of 25

forecast errors is given. Nevertheless, in particular, the DAF analysis shows a change
of magnitude that could indicate that the lockdown improved the day-ahead forecast.
One reason could be the smoother, less variable demand profile which was recognised in
Section 3.1. Even though the impact of the TSF change cannot be directly linked to the
lockdown due to minimal visible changes, in combination with the imbalance findings in
Section 3.3.3, the short-length forecast accuracy evidently decreased.

3.3.3. Imbalance Volume

An imbalance is prevalent in the power system when supply does not match demand.
If the imbalance is not tackled, it could lead to an unstable frequency and finally blackouts.
It is therefore the system operators (SO) responsibility to keep the balance in the system [11].
All accepted balancing measures in a settlement period are given by the net imbalance
volume (NIV), which represents the total sum of positive and negative system management
and energy balancing measure in the settlement period. In a perfect market, in any
settlement period the power plants are scheduled at gate closure (i.e., an hour before the
settlement period starts) with a target of NIV close to zero. However, that target is usually
not achieved because of:
• Demand prediction errors by suppliers.
• Generation prediction errors by generators (i.e., not able to tightly control the operation
of intermittent units).
• Problems in transmission lines.
• Balance must exist at every instant, but market trades in half-hour. settlement periods.
In Section 3.3.2, it is observed that the short-length load forecast accuracy decreased
slightly. The consequences of this poorer short-length forecast are amplified in the NIV
and cause the NIV to grow significantly compared to all other calendar weeks in 2019 and
2020, as seen in Figure 12. This indicates a change in the amount and volume of balancing
measures in the power system.

Weekly average share of Imbalance Volume

Specific imbalance volume 2020 Specific imbalance volume 2019
RES share 2020 RES share 2019
2.5% 80%
|Imbalance Volumne|/Actual Load

70%
2.0%
60%

1.5% 50%
RES share

40%
Lock-down
1.0% 30%

20%
0.5%
10%

0.0% 0%
2 3 4 5 6 7 8 9 10 11 12 13
Calendar Weeks

Figure 12. The weekly average share of imbalance volume factorised by the actual total load for 2019
and 2020. There is a significant imbalance increase during lockdown week.

After the lockdown, the higher share of RES seems to be the main driver for the
increasing imbalance in the power system. In Figure 13, the imbalance volume was
weighted to the actual total system demand for a pre- and post-lockdown week and
additionally shows the RES share for the same period. The correlation between weighted
Energies 2021, 14, 635 14 of 25

imbalance volume and the RES share after the lockdown is remarkable which indicates that
the increasing RES share is the main driver for the higher imbalance volume. The reason
for that cannot be precisely untangled as the following four points can cause an imbalance:
generation and demand prediction errors, network constraints and the balance need
at every instance. One possible high impact factor could be that the machine learning
approaches used by National Grid to forecast embedded RES output and load changes
together, had difficulty adapting [28]. However, analysing the past data reveals that the
correlation might not be permanent. When analysing data from January to March, only a
correlation between RES share and imbalance volume was discovered roughly at 20% of
the time.

Figure 13. Daily average share of imbalance volume and share of RES for the pre- and post-lockdown week, 2nd–9th
March and 22nd–28th March, respectively. There is a strong correlation between the post-lockdown RES share and
imbalance volume.

The effect of higher RES shares explains the slightly poorer performance of the short-
length load forecast TSF, as embedded RES, which consisted of 13 GW solar and 6 GW wind
capacity in 2018, cause load forecast errors [27,28]. On the contrary, the DAF improved,
so there might be a trade-off between the benefit of smoother load profiles and the negative
influence of high RES shares on the forecast errors. To summarise, it seems that depending
on the forecast length, the lockdown causes improved or worsened load forecast perfor-
mance (see Figure 14). The short-length load forecasts decrease in performance, while the
longer ones increase.
Energies 2021, 14, 635 15 of 25

Illustrative effect of COVID on the forecast accuracy

performance
Improved

DAF

+ smoother load profile

Forecast length
- higher share of RES
TSF
performance
Drop in

Imbalance
Volume

Figure 14. Illustrative effect of the lockdown on the forecast accuracy compared to pre-action weeks. TSF and DAF indicate
the transmission system forecast and total day-ahead forecast, respectively, as described in Section 3.3.2.

3.3.4. Loss of Load Probability

Loss of load probability (LOLP) is an indicator for system reliability measured by the
system operator for each settlement period [29]. For instance, when National Grid predicts
higher probability of loss of load, the balancing mechanism is willing to pay higher prices
for balancing at the time of reserve scarcity. The methodology to calculate the LOLP
can be found in [30]. The higher prices at high LOLP levels are also known as reserve
scarcity prices, which are the product of LOLP and the value of lost load (VoLL). The VoLL is
determined through the assessment of how much value consumers on average attribute to
the security of supply—currently £6000/MWh [29].

Reserve Scarcity Price = Loss o f Load Probability × Value o f Lost Load (2)

Due to the abrupt changes in the demand profile and eventually the inflexibility of the
available generation, National Grid predicted a higher LOLP during the evening of the 25th
of March which was the first official day of lockdown. Despite the fact that it was predicted
12 h in advance, the hour ahead LOLP forecast was 4.5 times higher. This implies that there
was a reserve scarcity and/or that the grid was under stress. On the 4th of March, due to
reserve scarcity, the system price increased to £2242/MWh in at 17:00 which is the highest
recorded value in the last 19 years and almost 20 times higher than the maximum system
price in February which was £120/MWh [7].

3.4. The Effects on Market Price

3.4.1. Day-Ahead Wholesale Market Price
The day-ahead market objective is to define a clearing energy price in which supply
meets the demand at any given hour of the day. To do so, a merit order model is used
to correctly dispatch power plants by sorting the existing generation units from low to
high marginal operating costs. Once the generation meets the demand curve the clearing
market price or equilibrium is achieved by minimising the generation cost [31]. Figure 15
shows graphically the process of the merit order model and the location of the clearing
Energies 2021, 14, 635 16 of 25

price as a result of the intersection of the supply and demand curves. The demand is
given as net demand which subtracts the RES generation from the total demand. This is a
common strategy to illustrate the merit order, since the solar and wind generation plants
have marginal cost close to zero, making them always dispatched as long as no network or
other operational constraints exist [15].

Market RES increase

=
Price Net demand
decrease
OCGT

RES increase
= CCGT
Wholesale market
price decrease

Nuclear
Biomass

Net demand = Capacity

Total demand – RES

Figure 15. Illustrative effects of the COVID-situation on the wholesale market price. The wholesale
market price reduces due to COVID. The higher RES share after the lockdown lowers the net demand
which leads to a wholesale market price reduction.

3.4.2. System Imbalance Price

The imbalance price is the price set by the system operator for every settlement
period to determine imbalance charges on electricity producers (generators) or consumers
(suppliers) [29]. The imbalance charge for a settlement period simply consists of the product
of imbalance volume (see Section 3.3.3) and imbalance price:

Imbalance chargeSP = Imbalance volumeSP × Imbalance priceSP

To evaluate the possible impact of the mitigation actions on the imbalance price, it is
analysed with regards to its components. The imbalance price is paid for the actions that
the system operator initiates to resolve the imbalance. However, the reason for initiating
a balancing action can vary. It could be either (i) an energy balancing or (ii) a system
balancing action in the 30-minute settlement period [29].
According to [29], the energy balancing action in the balancing mechanism (BM)
ensures that the energy amount of the physical notification is achieved. This is usually
priced by a bid-offer scheme. The merit-order ranking of the bids and offer is used to
reduce the balancing cost as the cheapest units are initiated first. This is not always possible
due to technical limitations of generators, demands and networks. An example for a
technical limitation of a BM generator is a non-suitable ramp-up rate, or a limitation for
the network such as an already congested line which would not allow more generation.
Therefore, not only the cheapest BM prices are selected by the system operator. In the BM,
units are usually priced by their utilisation cost. The cost of short-term operating reserve
(STOR), which can also participate in the BM service, is determined using the reserve
scarcity method which is a product of the Loss of Load Probability (LoLP) and Value of
Lost Load (VoLL). The resultant charge is the higher of the two pricing outcomes which are
namely utilisation price and reserve scarcity price. For instance, when the utilisation price
is 10 p/kWh and the reserve scarcity price is 15 p/kWh, the scarcity price is charged.
Energies 2021, 14, 635 17 of 25

For the purpose of energy balancing, the system operator might additionally purchase
non-BM services as the “Balancing Service Adjustment Action” [32]. Drivers for such
an adjustment action could be economic, technical or operational for ancillary services
according to [19]. These non-BM actions are priced in capacity, energy or both ways and
form balancing service adjustment data, consisting of the adjusted buy and sell prices,
which adjust the imbalance price of the previously described BM [19,32].
The system balancing actions, otherwise, are only a part of the non-BM actions [19].
They describe balancing actions which keep the energy equilibrium at every instance.
For example, a wind power plant might generate the exact energy amount contracted by
the physical notification for the 30-minute settlement period; however, its power might
fluctuate and mismatch the demand in the settlement period making system balancing
actions, such as activation of a non-BM STOR unit, necessary [19]. The pricing scheme for
the system balancing actions is equal to the energy balancing scheme for non-BM actions,
described in the previous paragraph.
Figure 16 illustrates the weekly average imbalance price development in 2019 and
2020. While both years show similar price variations, a significant price difference exists.
This is caused by a regulatory change of the balancing mechanism which started accepting
smaller generators in the range between 1–100 MW in 2020 [33,34]. This suppresses the
imbalance price through competition. The imbalance price during the lockdown is showing
an unclear trend. As described in the above paragraphs, the imbalance price is a complex
construct. Due to the substantial changes in the BM in 2019 and 2020, it is not possible to
draw a conclusion from this comparison to assess the COVID-19 lockdown effect.

Weekly average Imbalance Price

Imbalance price 2020 Imbalance price 2019
70

60
Imbalance price [£/MWh]

50

40

30
BM includes
20 since 2020
plants between
10 1-100 MW Lock-down

0
2 3 4 5 6 7 8 9 10 11 12 13 14
Calendar Weeks
Figure 16. The impact of lower demand on the imbalance price is not clear. Variation and trends observed in both 2019 and
2020 are similar. The observed price difference between the years is caused by wider access to balancing mechanism.

The impacts of the lower demand could potentially both increase and decrease the
imbalance price (see Figure 17). The balancing service options are generally chosen fol-
lowing a merit order if no system operation limitation exists. Therefore, similar to the
wholesale market price settlement, a higher demand would lead to higher prices and
cheaper generators could lower the price or vice versa. The imbalance volume and how it
Energies 2021, 14, 635 18 of 25

can increase the price is explored in Section 3.3.3, which imply a higher need for balancing
services. Moreover, on average, a lower demand (as shown in Section 3.1) would poten-
tially free up more generation units that can provide additional cost-effective balancing
services. As a result, the imbalance price could increase or decrease. Additional factors
also affect the price including the LoLP, VoLL, de-rated margin, voltage-related services
and network utilisation.

Potential effects of lower demand

on the imbalance price

More flexible
generators Higher
available imbalance
volume

Decrease in Increase in
imbalance price imbalance price

Figure 17. Illustrative example of the impact of lower demand on the imbalance price. Multiple
factors impact the imbalance price in different ways. One effect that reduces the imbalance price
is that more flexible conventional plants are available due to the lower demand and higher RES
share. In contrast, the observed higher imbalance volume increases the price as the more expensive
resources must be used (similar to merit order).

3.4.3. Variable Pricing for Domestic Consumers

The case for variable pricing for domestic consumers has been made by numerous
studies [35]. It is a more consumer-centric approach where the domestic consumers are
billed using the same half-hourly prices as the commercial ones rather than having a fixed
tariff (i.e., a volumetric calculation using a fixed £/kWh rate). There is also the commonly
known time-of-use (ToU) pricing where the £/kWh rate varies for different times of the day
which usually correlates to higher rates for higher demand periods. For instance, electricity
prices from 4 to 7 p.m. would be higher to reflect the evening peak whereas from 1 a.m. to
4 a.m. when the demand is usually low, the prices would be lower. Hence, this method of
pricing would also result in demand shifting.
British energy supplier Octopus [36] introduced their agile electricity tariff which is an
indexed half-hourly dynamic pricing that tracks the wholesale price of electricity (i.e., the
domestic rate changes every 30 min instead of a fixed monthly rate). On different occasions,
this resulted in negative pricing (i.e., the energy supplier paid its customers to consume
electricity). However, this also means that there is usually a steep price from 4 p.m. to
7 p.m. during the evening consumption surge. The following logic in Equation (3), is used
to determine the prime-time pricing. It uses the distribution cost coefficient (τ) multiplied
by the wholesale cost of electricity (W) and P which is the peak-time premium (which is
equal to 12 p/kWh during prime time). Then it caps the price to £35 p/kWh if the previous
outcome is higher than this value. This is because on average the fixed tariffs are in the
range of 15–20 p/kWh and it could be argued exposing domestic consumers to extreme
fluctuations in the system would be unfair.

min((τ × W + P), 35 p/kWh) (3)

In Figure 18, an example of capping at the maximum price of 35 p/kWh is shown

on the 4 March 2020 (i.e., during the pre-lockdown week). This day marks the first
time a system price was over £2000/MWh since 2001. It peaked at £2242/MWh [7]
Energies 2021, 14, 635 19 of 25

(See Section 3.3.4 for more information). The week commencing on the 30th of March 2020
is of interest for comparison with the other extreme, namely negative pricing, as it drops to
near −3 p/kWh. Similar to the analysis in Section 3.1, the reduction in demand magnitude
and changes in profile are correlated to the changes between pre- and post-action pricing
Octopus Agile Negative Pricing pre- and post-COVID-19 Mitigation Actions
profiles in Figure 18.
Date
2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06
35 Pre
Post

30

25
Price per unit (p/kWh)

20

15

10

5
2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09
Date

Figure 18. Examples of price capping (5 March 2020 on the lower orange x-axis) and negative pricing (5 April 2020 on the
higher green x-axis) during pre- and post-lockdown weeks, respectively, using the data from [37].

Since the launch of the agile tariff, there had been 96 occurrences of negative pricing
(i.e., price < 0 p/kWh). Almost 70% of these events (i.e., 67 out of 96) took place after the
lockdown started. Table 3 summarises the negative pricing events before and after the
lockdown, highlighting the highest price the consumers were paid to use electricity and
the corresponding dates.

Table 3. Analysis of negative pricing in the agile tariff using the data from [37].

Data Mean Min Max Dates Corresponding to Max Values

Pre (p/kWh) −1.62 −0.07 −4.85 9 December 2019
Post (p/kWh) −1.36 −0.02 −3.99 20 April 2020
Overall (p/kWh) −1.44 −0.02 −4.85 9 December 2019

Octopus also provides variable pricing for selling electricity [36]. The corresponding
sell prices are plotted in Figure 19. The highest sell price around 19 p/kWh was recorded
which corresponds to the day with the highest system price since 2001. The benefit is
passed on to the distributed generators. In the case of negative load pricing when the
consumers were paid to use electricity on the 5th of May, there was also negative pricing for
exporting electricity (i.e., generators pay to export electricity). The pricing for generation is
capped at a minimum of 0 p/kWh which indicates that the energy was exported for free
during that period as shown in Figure 19.
Energies 2021, 14, 635 20 of 25

Octopus Agile Negative Outgoing Pricing pre- and post-COVID-19 Mitigation Actions
Date
2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 2020-04-05 2020-04-06
20.0 Pre
Post
17.5

15.0
Price per unit (p/kWh)

12.5

10.0

7.5

5.0

2.5

0.0
2020-03-02 2020-03-03 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09
Date

Figure 19. Corresponding agile outgoing sell prices using the data from [37], that show a high sell price reflecting the
reserve scarcity (5 March 2020 on the lower orange x-axis) and a capped price of 0p/kWh (5 April 2020 on the higher
green x-axis).

4. Discussion
In Section 3, four main categories of results were presented, namely: (1) the changes
in demand, (2) generation portfolio, (3) forecasting and grid stability and lastly (4) market
prices. In this section, these results are evaluated and their impact on different stakeholders
are discussed. Following this, the limitations of the results are addressed along with
suggestions for future work.
The key results are summarised in Figure 20. They indicate that the grid is still reliable
and stable but operates under stress. More detailed explanations of each point here can be
found in Section 3.

Grid Stability
Demand Generation Load Forecast Market Prices
Indicators

Increasing supply and Lower wholesale

Lower demand More accurate
Higher VRE share demand mismatch market price due to
profile Day-Ahead forecast
(Δ+ 5 – 10 %)* (imbalance volume) VRE impact on
(~ Δ- 25 %)* (Δ+ 3 – 5 %)* merit-order
(Δ+ 0.7 %)*
Higher imbalance
Stable frequency, but volume, increases
Insignificant hourly
Smoother load Less operation of more prone to rapid imbalance price
forecast change
profile conventional plants changes since less More available
(Δ+- 0.1 %)* inertia on grid generators, lowers
imbalance price
Lower network loads in
Remarkable events most regions Beneficial agile
for loss of load Higher network load in pricing for some
probability (LoLP) some regions with high consumers
VRE share in network

Figure 20. Summary of key results. The asterisk (*) notes that the effect of some factors such as economic climate were not
taken into account. More details on whether these changes are representative are in Section 3.
Energies 2021, 14, 635 21 of 25

4.1. Implications for Stakeholders

The new disruptive and lower demand profile has multiple effects on the stakeholders
in the electricity system including network operators, suppliers, generators, aggregators,
DSR providers and consumers.
The network operators face a lower system demand on average which would result
in generally lower loading on the network yet occasionally higher network loads in some
cases. Firstly, a higher amount of balancing services are expected due to the decreased
performance in short-term forecasting (see Section 3.3.2) and hence, the resulting higher
volume of imbalances (see Section 3.3.3). Secondly, lower network loads are expected due
to the lower average load during the lockdown. However, at some particularly RES-rich lo-
cations the network load may increase due to the increased power flows (see Section 4.1.1).
In the wholesale market, suppliers interact with generators to form long-term elec-
tricity supply contracts [29]. The imbalance volume increased due to the lockdown which
most probably was unforeseen at the time of the contract. The increasing load uncertainty
and changes in the load profile may imply that both of these parties are subjected to more
imbalance charges. The trend of the total amount of imbalance charges cannot be easily
detected, as described in Section 3.4.2. The imbalance price is a complex measure which can
increase and decrease due to the lockdown effects. In addition to the imbalance, generators
and suppliers would suffer from the disruptive demand changes in terms of profitability.
The share of fixed cost compared to amount of sales may increase to compensate for the
lower volume of electricity demanded. This eventually would lead to higher electricity
prices for domestic and industrial consumers. On the contrary, suppliers are expected
to benefit from lower wholesale market prices. For instance, as the demand decreases,
the supplier would need to purchase a smaller volume of energy resulting a lower cost
whilst receiving fixed instalments from domestic customers (see Section 3.4.1).
Aggregators and DSR providers are predicted to be scheduled more often by the
system operator because of the larger imbalance volume observed during the lockdown.
The only requirement for them is to be more competitive than the flexibility offered from
the non-scheduled power plants as more plants are expected to be available for such
services due to decreased baseline consumption. If so, aggregators and DSR providers
would benefit from the COVID-19 lockdown.
There is virtually no benefit for the distributed consumers with a fixed rate supply
agreement as the average domestic household demand is expected to increase due to WFH.
However, there has been approximately 70 negative pricing events since the lockdown
started which suggests that consumers with variable pricing such as the agile tariff are
getting paid to use electricity. Such consumers can also take advantage of reserve scarcity
and benefit from exporting when the grid is under stress (see Section 3.4.3 for more details).
Regarding the commercial and industrial users, the same would apply which indicates that
the users with the most flexible assets/loads would be able to take advantage of the effects
of the lockdown on the pricing.

4.1.1. Implications for Future Systems

The demand and generation findings for the lockdown state of the electricity system
can be used as a representation of the next decade according to the International Energy
Agency [38]. This would be when the share of RES, such as from solar and wind, is higher
and balancing services are more in demand. Therefore, this suggests that the results of this
study (see Figure 20) regarding the effect of the lockdown due to the COVID-19 pandemic
can be interpreted as an outlook into the future.
As suggested by several indicators, when using the findings of this paper as a future
scenario, several assumptions and limitations must be noted. Firstly, the lockdown changed
the load profile shape which resulted in a flatter profile that is different than a typical
future demand scenario which may assume more efficient home appliances but increase
in electric vehicle ownership (i.e., increase in over-night demand). This flatter demand
profile, counter-intuitively, led to better day-ahead load forecast even though the share of
Energies 2021, 14, 635 22 of 25

RES increased. Secondly, the current power system is supported by inflexible nuclear and
gas plants which might change in the future due to the increasing amount of flexibility
services such as DSR. Lastly, the current network is comparatively oversized due to the
lower load. The same network at higher RES share could lead to more congestion in a
future power system.
One of the most important aspects of planning for a future power system is consid-
eration of network congestion that is expected to occur as RES share increases. During a
lockdown, the network usage is expected to decrease on average but some sections might
be loaded more than usual. The lower average demand in GB implies that less energy is
transported through the power lines which results in reduced network usage and losses.
However, loading of some other network sections could increase as centralised renewable
energy sources would transport energy for longer distances. For instance, the large wind
generation capacity installed in Scotland provides more energy to the southern parts such
as London. As the net consumption in GB decreases, less energy is locally consumed in the
north and may lead to higher network loads along the transmission lines when there is
generation from wind and/or solar. Therefore, despite the decreased load, some parts of
the network are likely to experience higher congestion.
For data with high temporal resolution such as frequency and demand, there are many
variables that affect long-term (i.e., 2019 vs. 2020) comparison including weather conditions,
availability of generators, increase in demand and generation, network management
actions, etc. This is a known challenge that has been the subject of previous studies such
as [39]. Hence, the long-term comparison is limited to the weekly average values in this
study. In terms of short-term comparative analysis (i.e., pre- and post-lockdown days—up
to a week), the effect of the weather data such as humidity, temperature and wind speed
are not taken into account. This is because the effect of weather conditions and other
socio-economic impacts are observed to be less significant in comparison to the effect of
this major socio-economic event, namely the lockdown due to the spread of COVID-19
in GB.

4.2. Outlook and Future Work

Overall, this analysis reveals a significant point for future models of a low probability
but high impact event such as the 2019 pandemic. This is that the imbalance increases and
stresses the grid if the operator is not prepared. This has the potential to result in a record
high system price (such as the £2,242/MWh mentioned previously) and/or a more severe
problem such as a blackout in the future power system. The main findings, which are
summarised in Figure 20, can be used for modelling the demand, generation, market and
grid stability for a future low probability high impact events. Variable pricing for domestic
consumers gives price signals that can beneficially change demand profiles. As shown
in Section 3.4.3, the lockdown has led to almost 70 events of negative pricing where the
users were rewarded for their consumption. At the same time, the distributed generators
benefited from some high sell prices during the lockdown. The introduction of half-hourly
indexed variable pricing encouraged peak-shaving during high demand periods where
prices are expensive and more consumption during energy surplus times where prices
are low and sometimes negative. As a consumer-centric innovation, the variable pricing
approach can function as a gateway for emergent markets such as peer-to-peer trading
which involves the exchange of electricity amongst distributed consumers to increase their
self-sufficiency [35] proves that even consumers not participating in DSR actions would
benefit from real-time and variable pricing. Using disruptive technologies such as home
energy management systems, smart metering and internet of things (IoT) loads can respond
to price signal and adjust their scheduling in order to minimise their electricity cost. This is
especially significant when charging electric vehicles as discussed in [40]. On the grid scale,
studies such as [41] prove the significance of scheduling algorithms for taking advantage
of both arbitrage and other DSR events and highlight the future potential of commercial
size battery energy storage systems.
Energies 2021, 14, 635 23 of 25

The outcomes of this analysis may be used for predicting the response of the electricity
market to another low probability and high impact event in the future.

5. Conclusions
The outbreak of COVID-19 disrupted the patterns in electricity consumption, challeng-
ing the system operations of forecasting and balancing the supply and demand. This is due
to the mitigation measures that include lockdown and WFH which decreased the aggregate
demand by 25% and remarkably flattened its profile. These changes were characterised
with various quantitative markers and compared with pre-lockdown business-as-usual
data using the case study of Great Britain. Similar observations have been made in different
countries such as Australia [42] and Italy [43].
The ripple effects on the generation portfolio revealed a 5 to 10% higher RES share
and decreased operation of conventional plants. The system stability indicators suggest
that the grid operated well but was under stress. The indicators include some remarkable
LoLP events and overall higher system frequency. However, other contrasting findings
show 3 to 5% more accurate day-ahead load forecasts. The energy market is also greatly
affected by the change in consumption pattern. The wholesale market price decreased due
to RES generators ranking higher on the merit order. Whilst the imbalance prices increased
due to the higher imbalance volume in the system, this increase was compensated by the
larger number of available generators due to the decreased demand volume.
An alternative pricing mechanism was also investigated for domestic consumers.
With over 70 events of negative pricing, it was shown that the new pricing scheme would
have benefited consumers with flexible load such as an EV. Despite the overall drop in the
prices due to the decrease in wholesale market price, there were some LoLP events that
increased the system price as much as £2242/MWh which is the highest in the last 19 years
and almost 20 times higher than the month preceding the lockdown (February 2020).
Four main categories of results presented, namely: (1) the changes in demand, (2) gen-
eration portfolio, (3) forecasting and grid stability and lastly (4) market prices Section 4
assessed their impact on different stakeholders such as system operators, suppliers and con-
sumers. Following this, the limitations of the results were addressed along with suggestions
for future work.
The proposed open-source energy data extraction and pre-processing pipeline can be
used in a variety of similar studies—see Figure 1. It can be useful for both academic and
industrial research in electricity markets, trades and forecasts as it simplifies the procedures
of data extraction, pre-processing and visualisation.

Author Contributions: Conceptualization, D.K., M.P. and A.K.; methodology, D.K.; software, D.K.;
validation, D.K., M.P. and A.K.; formal analysis, D.K and M.P.; investigation, D.K. and M.P.; resources,
D.K., M.P. and A.K.; data curation, D.K.; writing—original draft preparation, D.K. and M.P.; writing—
review and editing, D.K., M.P. and A.K.; visualization, D.K. and M.P.; supervision, A.K.; project
administration, D.K. and A.K.; funding acquisition, A.K. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by EPRSC Doctoral Training Partnership (EP/R513209/1) and
the EPSRC Centre for Energy System Integration (EP/P001173/1).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are openly available in DataShare at
https://doi.org/10.7488/ds/2979 [9].
Acknowledgments: The authors would like to acknowledge Elexon and Energy Stats UK as this
paper contains BMRS data © (Elexon Limited copyright and database right [2020]) and Octopus
Agile tariff data provided by the energy-stats.uk website.
Energies 2021, 14, 635 24 of 25

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.

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